Journal of Experimental Psychology: Human
Perception and Performance
The Spatiotemporal Dynamics of Scene Gist Recognition
Adam M. Larson, Tyler E. Freeman, Ryan V. Ringer, and Lester C. Loschky
Online First Publication, November 18, 2013. doi: 10.1037/a0034986
CITATION
Larson, A. M., Freeman, T. E., Ringer, R. V., & Loschky, L. C. (2013, November 18). The
Spatiotemporal Dynamics of Scene Gist Recognition. Journal of Experimental Psychology:
Human Perception and Performance. Advance online publication. doi: 10.1037/a0034986
Journal of Experimental Psychology:
Human Perception and Performance
2013, Vol. 39, No. 6, 000
© 2013 American Psychological Association
0096-1523/13/$12.00 DOI: 10.1037/a0034986
The Spatiotemporal Dynamics of Scene Gist Recognition
Adam M. Larson
Tyler E. Freeman, Ryan V. Ringer, and
Lester C. Loschky
University of Findlay
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Kansas State University
Viewers can rapidly extract a holistic semantic representation of a real-world scene within a single eye
fixation, an ability called recognizing the gist of a scene, and operationally defined here as recognizing
an image’s basic-level scene category. However, it is unknown how scene gist recognition unfolds over
both time and space—within a fixation and across the visual field. Thus, in 3 experiments, the current
study investigated the spatiotemporal dynamics of basic-level scene categorization from central vision to
peripheral vision over the time course of the critical first fixation on a novel scene. The method used a
window/scotoma paradigm in which images were briefly presented and processing times were varied
using visual masking. The results of Experiments 1 and 2 showed that during the first 100 ms of
processing, there was an advantage for processing the scene category from central vision, with the
relative contributions of peripheral vision increasing thereafter. Experiment 3 tested whether this pattern
could be explained by spatiotemporal changes in selective attention. The results showed that manipulating the probability of information being presented centrally or peripherally selectively maintained or
eliminated the early central vision advantage. Across the 3 experiments, the results are consistent with
a zoom-out hypothesis, in which, during the first fixation on a scene, gist extraction extends from central
vision to peripheral vision as covert attention expands outward.
Keywords: scene gist, scene categorization, spatiotemporal processing, attention, time course
Treyens, 1981; Pezdek, Whetstone, Reynolds, Askari, & Dougherty, 1989).
Scene gist recognition has been operationalized in numerous
ways, but usually in terms of the ability to classify a briefly flashed
scene image at some level of abstraction, from the highly specific
(e.g., “a baby reaching for a butterfly”; Fei-Fei et al., 2007;
Intraub, 1981; Potter, 1976, pp. 509 –510), to the basic-level scene
category (e.g., front yard; (Loschky, Hansen, Sethi, & Pydimari,
2010; Malcolm et al., 2011; Oliva & Schyns, 2000; Renninger &
Malik, 2004; Rousselet et al., 2005), to the superordinate-level
scene category (e.g., natural; Goffaux et al., 2005; Greene & Oliva,
2009; Joubert, Rousselet, Fize, & Fabre-Thorpe, 2007; Loschky &
Larson, 2010), to whether the scene contains an animal (BaconMacé, Macé, Fabre-Thorpe, & Thorpe, 2005; Evans & Treisman,
2005; Fei-Fei, VanRullen, Koch, & Perona, 2005; Kirchner &
Thorpe, 2006; Rousselet, Fabre-Thorpe, & Thorpe, 2002), to the
scene’s emotional valence (e.g., positive; Calvo, 2005; Calvo,
Nummenmaa, & Hyona, 2008). Thus, the theoretical construct of
scene gist has been operationalized in many different ways regarding the semantic information that viewers acquire from a scene.
Similar to many previous studies, the current study operationally
defines scene gist recognition as viewers’ ability to accurately
categorize real-world scenes at the basic level (e.g., Joubert et al.,
2007; Loschky & Larson, 2010; Loschky et al., 2007, 2010;
Malcolm et al., 2011; McCotter, Gosselin, Sowden, & Schyns,
2005; Oliva & Schyns, 2000; Rousselet et al., 2005).
As noted, a fundamental constant in the scene gist construct is
that the specified semantic information is acquired within a single
fixation. The fact that scene gist acquisition occurs within a single
eye fixation has been shown by eye movement research in which
the very first eye movement in a visual search task, which imme-
When viewing a scene image, people may describe it with such
terms as “a bedroom” or “a person raking leaves.” Viewers can
arrive at this semantic categorization of a real-world scene extremely rapidly—within a single eye fixation—and the theoretical
construct for this process is often called “scene gist recognition”
(Biederman, Rabinowitz, Glass, & Stacy, 1974; Castelhano &
Henderson, 2008; Fei-Fei, Iyer, Koch, & Perona, 2007; Greene &
Oliva, 2009; Larson & Loschky, 2009; Loschky & Larson, 2010;
Malcolm, Nuthmann, & Schyns, 2011; Oliva & Torralba, 2006;
Potter, 1976; Rousselet, Joubert, & Fabre-Thorpe, 2005). The
scene gist construct is important for theories of scene perception
because recognizing the gist of a scene affects later theoretically
important processes such as attentional selection (Eckstein, Drescher, & Shimozaki, 2006; Gordon, 2004; Torralba, Oliva, Castelhano, & Henderson, 2006), object recognition (Bar & Ullman,
1996; Biederman, Mezzanotte, & Rabinowitz, 1982; Boyce &
Pollatsek, 1992; Davenport & Potter, 2004; but see Hollingworth
& Henderson, 1998), and long-term memory for scenes (Brewer &
Adam M. Larson, Department of Psychology, University of Findlay;
Tyler E. Freeman, Ryan V. Ringer, and Lester C. Loschky, Department of
Psychological Sciences, Kansas State University.
Author contributions: All authors conceived and designed the experiments; the experiments were performed by Adam M. Larson and Ryan V.
Ringer; the data were analyzed by Adam M. Larson, Tyler E. Freeman, and
Ryan V. Ringer; and Lester C. Loschky, Adam M. Larson, Tyler E.
Freeman, and Ryan V. Ringer contributed to writing the article.
Correspondence concerning this article should be addressed to Adam M.
Larson, Department of Psychology, 200 Howard St., University of Findlay,
Findlay, OH 45840. E-mail: larson@findlay.edu
1
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
2
LARSON, FREEMAN, RINGER, AND LOSCHKY
diately follows the first fixation (typically placed at the center of
the image prior to onset of the image), generally goes directly to an
expected location based on the semantic category of the scene. For
example, it has been shown when the search target was “chimney,”
the first saccade on a scene often went directly to the roof of a
house, even when there was no chimney in the picture (Eckstein et
al., 2006; see also Torralba et al., 2006). Other studies have
strongly suggested that gist recognition occurs within a single
fixation by presenting extremely briefly flashed and backwardmasked scene images (to control processing time) and asking
viewers to categorize the scenes at the basic level. These studies
have shown asymptotic basic-level scene categorization performance at stimulus onset asynchronies (SOAs) of 100 ms (Biederman et al., 1974; Potter, 1976) and an inflection point after SOAs
as little as 35 to 50 ms (Bacon-Macé et al., 2005; Loschky &
Larson, 2010; Loschky, Larson, Smerchek, & Finan, 2008;
Loschky et al., 2007, 2010), which is far less than the average
duration of a fixation during scene viewing of 330 ms (Rayner,
1998). Thus, in order to build on the findings from the latter
studies, the current study investigates the time course of scene gist
processing by varying processing time over a wide range of
masking SOAs whose maximum is roughly equal to a single
fixation.
Although much is known about how humans process and represent the gist of scenes, a key unknown is how scene gist recognition unfolds over both time and space—within a single eye
fixation and across the visual field. Specifically, because viewers
acquire scene gist within the temporal window of a single fixation,
any given piece of visual information in a scene has a fixed retinal
eccentricity during that critical first fixation. Thus, because retinal
eccentricity greatly affects visual processing (for review, see Strasburger, Rentschler, & Juttner, 2011; Wilson, Levi, Maffei, Rovamo, & DeValois, 1990), the retinal eccentricity of scene information must play a key role in scene gist acquisition (Larson &
Loschky, 2009). However, this raises the novel question addressed
in this study: Does the spatial variability in processing across the
visual field undergo important changes over the time course of the
critical first fixation on a scene? Interestingly, three plausible
alternative hypotheses regarding such spatiotemporal variability in
scene gist processing are suggested by the existing literature.
The first hypothesis stems from the well-known differences in
the speed of information transmission between central and peripheral vision from the retina to the brain, with peripheral visual
information reaching the lateral geniculate nucleus (LGN) of the
thalamus and the primary visual cortex (V1) before central visual
information (Nowak, Munk, Girard, & Bullier, 1995; Schmolesky
et al., 1998). Because peripheral vision is particularly important for
scene gist recognition (Larson & Loschky, 2009), it is possible and
plausible that this temporal processing advantage for peripheral
vision may underlie the incredible speed of gist recognition (Calvo
et al., 2008; Girard & Koenig-Robert, 2011).
A second plausible alternative hypothesis is based on eye movement and attention research, which has shown that covert attention
starts centrally in foveal vision at the start of each fixation, and,
over time, extends out to the visual periphery (Henderson, 1992;
White, Rolfs, & Carrasco, in press). This central-to-peripheral
spatiotemporal order of visual processing could extend to the
process of scene gist recognition during the critical first fixation on
a scene, producing an advantage for central vision early in the first
fixation.
Finally, a third plausible alternative hypothesis is based on the
idea that the rapid extraction of scene gist within a single fixation
occurs in the near absence of attention (Fei-Fei et al., 2005; Li,
VanRullen, Koch, & Perona, 2002; Otsuka & Kawaguchi, 2007;
Rousselet et al., 2002) and in parallel across the field of view
(Rousselet, Fabre-Thorpe, & Thorpe, 2002). In that case, neither
central nor peripheral vision would be expected to play a larger
role early or late in processing scene gist, but instead would be
assumed to play equivalent roles throughout the first fixation.
These three plausible alternative hypotheses cover three logical
possibilities, either (a) an early advantage for peripheral over
central vision, (b) the reverse, namely, an early advantage for
central over peripheral vision, or (c) no advantage for either central
or peripheral vision. Below, we describe the research supporting
each of these alternative hypotheses in greater detail.
Central Versus Peripheral Vision and the
Spatiotemporal Dynamics of Scene Gist Recognition
The visual field can be roughly divided into two mutually
exclusive regions: central and peripheral vision. Central vision,
which includes both foveal and parafoveal vision,1 is contained
within a roughly 5° radius of fixation (Osterberg, 1935; cited in
Strasburger et al., 2011, p. 3). We follow standard convention in
studies of visual cognition by defining peripheral vision as the
remainder of the visual field beyond central vision’s 5° radius
(e.g., Hollingworth, Schrock, & Henderson, 2001; Holmes, Cohen,
Haith, & Morrison, 1977; Rayner, Inhoff, Morrison, Slowiaczek,
& Bertera, 1981; Shimozaki, Chen, Abbey, & Eckstein, 2007; van
Diepen & Wampers, 1998).
Our first of three alternative hypotheses regarding the spatiotemporal dynamics of scene gist acquisition is related to the fact
that the vast majority of information in real-world scenes is contained within peripheral vision, which has lower spatial resolution
but a finer temporal resolution and faster information transmission
to visual cortex than central vision (Livingstone & Hubel, 1988;
Nowak et al., 1995; Strasburger et al., 2011; Wilson et al., 1990).
Neurophysiological studies of macaques have shown that visual
information transmitted by the magnocellular retinal ganglion cells
reaches the LGN and V1 approximately 20 ms faster than information transmitted by the parvocellular retinal ganglion cells
(Nowak et al., 1995; Schmolesky et al., 1998). This advantage has
been estimated to be substantially larger (90 ms) for peripheral
vision in humans, as shown for discrimination of Gabor orientation
in central vision (4° eccentricity) versus peripheral vision (10°
eccentricity) (Carrasco, McElree, Denisova, & Giordano, 2003).
The visual transmission advantage for peripheral vision could be
critical for processing real-world scene images, including the recognition of a scene’s basic-level category, especially at the early
stages of scene processing.
1
Although these two visual areas are referred to as central vision, there
are important anatomical and perceptual differences between them. The
fovea contains the greatest concentration of cones (Curcio, Sloan, Packer,
Hendrickson, & Kalina, 1987), whereas the parafovea has the largest
concentration of rods (Curcio, Sloan, Kalina, & Hendrickson, 1990). Likewise, visual acuity is greater in the fovea than the parafovea (Westheimer,
1982; Wilson et al., 1990).
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
SPATIOTEMPORAL SCENE GIST DYNAMICS
Recent studies have shown the importance of peripheral vision
for processing real-world scene images. Larson and Loschky
(2009) showed that the central 5° of an image could be completely
removed from a scene with no decrease in basic-level scene
categorization performance. Conversely, presenting only the central 5° of a scene, while blocking scene information beyond that,
produced worse categorization performance than when the entire
scene image was presented. Similarly, Boucart and colleagues
(Boucart, Moroni, Thibaut, Szaffarczyk, & Greene, 2013) showed
the usefulness of peripheral vision for scene categorization by
presenting scene images to the left and right of fixation and having
viewers indicate the side with the target category. Performance
was good (73% accuracy), even for scenes presented at up to 70°
eccentricity. Similar results have been shown for animal detection
in scenes using far peripheral vision (Thorpe, Gegenfurtner, FabreThorpe, & Bulthoff, 2001). These results show that peripheral
vision conveys critical information for basic-level scene categorization despite its low spatial resolution. Given that information
from peripheral vision is transmitted to the LGN and V1 faster
than information presented in central vision, this could produce
better scene gist recognition in peripheral vision than central vision
at the earliest stages of scene processing (Calvo et al., 2008; Girard
& Koenig-Robert, 2011).
Interestingly, a separate body of literature on attention in scenes
suggests a second alternative hypothesis regarding the spatiotemporal processing of scene gist in the first fixation. Henderson
(1992) has argued for the sequential attention model, in which
attention starts in central vision for each eye fixation and is later
sent to the target of the next saccade in the visual periphery toward
the end of the fixation. Research on reading processes has shown
evidence consistent with this hypothesis (Rayner et al., 1981;
Rayner, Liversedge, & White, 2006; Rayner, Liversedge, White, &
Vergilino-Perez, 2003), and later research found similar findings
for visual search in scenes and scene memory (Glaholt, Rayner, &
Reingold, 2012; Rayner, Smith, Malcolm, & Henderson, 2009; van
Diepen & d’Ydewalle, 2003). For example, van Diepen and
d’Ydewalle (2003) found that in a nonobject search task, masking
foveal information early in a fixation was more detrimental than
masking peripheral information. This deleterious effect was observed with both the search task and eye movement measures,
suggesting that at the beginning of each fixation, information from
the center of vision was processed first, followed by the information contained in the visual periphery. However, van Diepen and
d’Ydewalle (2003) argued that their findings might not apply to
other tasks:
Given the task demands in the present experiments, objects of moderate size were of primary importance, and had to be inspected
foveally. Conceivably, in other tasks a much larger part of the stimulus is processed at the beginning of fixations (e.g., when the scene
identity has to be determined). Obviously, in the latter tasks the area
that will affect fixation durations can be expected to be much larger
than just the foveal stimulus. (p. 97; emphasis added)
That is, although there is evidence of the sequential allocation of
attention from central to peripheral vision during extended visual
search in scenes, it may not apply to the rapid acquisition of scene
gist at the beginning of the first fixation on a scene, when peripheral vision may be more important.
3
A more recent study by Glaholt et al. (2012) further tested the
sequential attention model with real-world scenes in both visual
search and scene recognition memory tasks. In that study, either
central vision or the entire image was masked, gaze contingently,
after varying SOAs within the first 100 ms of each fixation
(Glaholt et al., 2012). That study showed that loss of central vision
(with a 3.71°-radius mask) only affected early processing. For
scene recognition memory, a central mask with a 0-ms masking
SOA reduced performance, but a 50-ms SOA did not. For visual
search, a central mask with a 50-ms masking SOA degraded foveal
target identification, but a 100-ms SOA did not. In contrast,
masking the entire image disrupted both visual search and scene
recognition memory as late as a 75-ms masking SOA. Thus,
whereas central vision was only important early in a fixation,
peripheral vision remained important until later in fixations for
both visual search and scene recognition memory. These results
are consistent with the sequential attention model, but do not speak
to van Diepen and d’Ydewalle’s (2003) argument that the fovealto-peripheral sequential attention model may not apply to scene
gist recognition on the first fixation on a scene. Thus, this remains
an important untested hypothesis regarding the spatiotemporal
dynamics of scene gist acquisition.
An interesting question is whether the sequential attention
model can be interpreted as a spatiotemporally constrained version
of the zoom-lens model (Eriksen & St. James, 1986; Eriksen &
Yeh, 1985), in which covert attention zooms out on each fixation.
Regardless, if the sequential attention model indeed applies to
scene gist recognition on the first fixation on a scene (contrary to
van Diepen & d’Ydewalle’s, 2003, suggestion), then it suggests
that basic-level scene categorization should be best in central
vision at the early stages of a fixation, whereas performance should
converge at later processing times.
Finally, a third alternative hypothesis regarding the spatiotemporal dynamics of scene gist acquisition is suggested by research
on the role of attention in scene gist acquisition. This research
questions whether attentional processes— here, the spatiotemporal
dynamics of visual attention across the visual field— underlie
scene gist recognition, or whether preattentive processes, which
work across the entire field of view in parallel, underlie scene gist
recognition. Several recent studies have suggested that scene categorization requires little, if any, attentional resources (Fei-Fei et
al., 2005; Li et al., 2002; Otsuka & Kawaguchi, 2007; Rousselet et
al., 2002), whereas other studies suggest that attention may yet
play a role in obtaining meaningful scene information (Cohen,
Alvarez, & Nakayama, 2011; Evans & Treisman, 2005; Walker,
Stafford, & Davis, 2008).
If scene gist recognition requires attention and is affected by
attentional processes, then the spatiotemporal dynamics of attention during the initial fixation on a scene should produce differences in basic-level scene categorization between central and peripheral vision at early processing times. Conversely, if scene gist
acquisition is an attention-free process, based primarily on preattentive processes operating in parallel across the entire field of
view (Fei-Fei et al., 2005; Li et al., 2002; Otsuka & Kawaguchi,
2007; Rousselet et al., 2002), then no differences should be found
between the utility of central versus peripheral vision for scene gist
acquisition over the time course of the critical first fixation. This
constitutes a well-founded and plausible null hypothesis regarding
4
LARSON, FREEMAN, RINGER, AND LOSCHKY
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
the role of the spatiotemporal dynamics of visual attention in scene
gist acquisition.
In sum, the goals of the current study were to determine (a)
whether there is any difference in the utility of central versus
peripheral vision in acquiring the basic-level scene category of a
scene over the time course of a single fixation, and (b) if such
differences exist, whether they are more consistent with the idea
that peripheral vision is processed most quickly, and thus dominates early scene categorization, or whether processing expands
from central vision outward over the course of a single fixation
consistent with the sequential attention model.
General Method of the Study
The current study used a “window” and “scotoma” paradigm
(see Figure 1) to evaluate the relative contributions of central
versus peripheral vision to scene gist recognition over time (Larson & Loschky, 2009). We define a window as a circular viewable
region encompassing the central portion of a scene, while blocking
the more eccentric peripheral information (McConkie & Rayner,
1975; van Diepen, Wampers, & d’Ydewalle, 1998). Conversely, a
scotoma blocks out the central portion of a scene and shows only
the peripheral information (Rayner & Bertera, 1979; van Diepen,
et al., 1998). An inherent difficulty in such a method is that any
difference in scene categorization could potentially be explained
simply in terms of a difference in the amount of viewable information available in each condition. For example, if it were shown
that peripheral vision had an advantage over central vision, one
could argue that this was because peripheral vision had more
information. Conversely, if central vision showed an advantage,
this advantage could similarly be argued to be due to cortical
magnification of foveal and parafoveal information. Thus, to control for such potentially confounding spatial attributes inherent to
central and peripheral vision, it is first necessary to determine what
we call the “critical radius”—that is, the radius that perfectly
divides the central and peripheral regions of a scene into two
mutually exclusive regions, each of which produces equivalent
scene categorization performance when given unlimited processing time within a single fixation (i.e., when images are unmasked,
and thus sensory memory generally lasts until a saccade is made;
Larson & Loschky, 2009). Given a critical radius for which unlimited processing time in a single fixation produces equal performance for information presented in both window and scotoma
conditions, then we can ask whether limiting processing time
produces any difference in scene categorization performance between those window and scotoma conditions based on the critical
radius.
It is important to note from the outset that such a research
strategy is highly conservative, with a strong bias toward finding
no difference between the window and scotoma conditions, given
that the critical radius is defined in terms of producing equivalent
performance between the two conditions (when there is no masking). Therefore, if variations in processing time do produce differences even when using the critical radius, then we can be confident
that those differences do not stem from an imbalance in the amount
of image content provided within the window and scotoma conditions, respectively. It is for this reason that we took the conservative strategy of using the critical radius to balance the functional
value of viewable imagery in the window and scotoma conditions.
General Hypotheses
If the greater neural transmission speed of peripheral vision
influences early differences in scene gist acquisition, then we
would expect a scene categorization performance advantage for
the scotoma condition over the window condition at early processing times. Conversely, if the sequential attention model applies to
acquiring scene gist over the course of a single fixation, then we
would expect a scene categorization performance advantage for
the window condition at early processing times compared with the
scotoma condition. Finally, if scene gist acquisition is a largely
parallel and preattentive process, then window and scotoma conditions should be equally useful for scene categorization throughout the critical first fixation, and varying the processing times for
window and scotoma conditions should produce no advantage for
either condition, whether early or late in processing.
We conducted three experiments to explore the spatiotemporal
dynamics of scene gist acquisition in a single fixation and their
relationship to visual attention. Across the three experiments, our
data suggest that at the beginning of a fixation, attention is allocated to central vision. However, within the first 100 ms of scene
processing, attention expands to encompass peripheral areas of the
visual field. These findings are consistent with a combination of
the sequential attention model and a spatiotemporally constrained
interpretation of the zoom lens model of attention that we call the
zoom-out hypothesis. These novel results, and the theoretical advance they provide, place fundamental spatiotemporal constraints
on any theory of the processes involved in scene gist acquisition.
Experiment 1
Figure 1. Example image in a window and a scotoma viewing condition.
Note that the radius for the window is the same as the radius for the
scotoma. This is the critical radius used in the current study, which
produces equal scene gist recognition in both the window and scotoma
conditions when presented without a visual mask (i.e., when given unlimited processing time within a single fixation).
Experiment 1 was designed to investigate the relative utility of
information in central versus peripheral vision for scene gist acquisition over the time course of single fixation. We used a
window/scotoma paradigm to selectively present scene information to either central or peripheral vision, respectively, together
with visual masking to vary processing time. This enabled us to
test our three competing hypotheses that the transmission speed
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
SPATIOTEMPORAL SCENE GIST DYNAMICS
advantage for peripheral vision, the sequential attention model, or
parallel and preattentive processes across the visual field would
best explain the spatiotemporal dynamics of scene gist recognition.
Our window and scotoma stimuli were constructed using a
critical radius that produced equivalent basic-level scene categorization in both the window and scotoma conditions when the
stimuli were unmasked, in order to functionally equalize the viewable information presented in the window and scotoma conditions.
By using the critical radius, however, we greatly reduced the
chances of rejecting the null hypothesis when comparing the
window and scotoma conditions. Thus, even relatively small differences found between the two conditions as a function of SOA
would indicate differences in the spatiotemporal dynamics of
scene gist recognition.
Method
Participants. There were 56 participants (33 female), whose
ages ranged from 18 to 32 years old (M ⫽ 19.59, SD ⫽ 2.02). All
had normal or corrected-to-normal vision (20/30 or better), gave
their institutional review board-approved informed written consent, and received course credit for participating.
Design. The experiment used a 2 (window vs. scotoma) ⫻ 6
(processing time) within-subjects design. There were 28 practice
trials, followed by 240 recorded trials.
Stimuli. Window and scotoma stimuli were created from circularly cropped scene images having a diameter of 21.9° (i.e., a
maximal radius/retinal eccentricity of 10.95°) at a viewing distance of 63.5 cm, using a forehead and chin rest. We interpolated
the size of the critical radius based on the prior results of Larson
and Loschky (2009), and confirmed through pilot testing that a
critical radius of 5.54° (170 pixel radius) produced equal performance in both the window and scotoma conditions when stimuli
were presented for 24 ms unmasked. Window images presented
25.6% of the viewable scene area inside the critical radius, while
74.4% of the viewable scene area was presented outside the critical
radius in the scotoma condition. We used a total of 240 images,
which were comprised of 10 scene categories (five natural: beach,
desert, forest, mountain, river; five man-made: farm, home, market, pool, street). Thus, the 240 scene images were randomly
assigned to each viewing condition, and each scene was presented
only once. The circular scene stimuli were presented on a 17-in.
ViewSonic Graphics Series CRT monitor (Model G90fb).
Masks were scene texture images generated using the Portilla
and Simoncelli (2000) algorithm. These types of masks have been
shown to be highly effective at disrupting scene gist processing
because they contain second-order and higher order image statistics similar to real-world scenes but do not contain any recognizable information (Loschky et al., 2010). Masks were identical in
shape and size to the stimuli they masked—window stimuli were
masked by window masks, and scotoma stimuli were masked by
scotoma masks (see Figure 2). This was done to avoid metacontrast masking, which tends to produce Type B (u-shaped) masking
functions (Breitmeyer & Ogmen, 2006; Francis & Cho, 2008). All
images, including targets and masks, were equalized in terms of
their mean luminance and Root Mean Square (RMS) contrast (see
Loschky et al., 2007, for details on equalizing mean luminance and
RMS contrast). Scene information contained outside the window,
or inside the scotoma, was replaced by neutral gray equal to the
5
mean luminance value of our stimuli. The same gray value was
used for the blank screens and the background of the fixation point
and category label.
Procedures. After completing a preliminary visual near acuity
test (using a Snellen chart), participants were seated in front of a
computer monitor. Participants were first familiarized with the 10
scene categories by showing them four sample images from each
category together with their respective category labels. Participants
then completed 30 unrecorded practice trials before completing the
240 experimental trials. The sample and practice stimuli were not
used again in the main experiment.
Figure 2 shows a schematic of a trial in each of the two
viewing conditions (window vs. scotoma). We used an EyeLink
1000 remote eyetracking system with a forehead and chin rest
to maintain a constant viewing distance. The eyetracker was
programmed with a “fixation failsafe” algorithm to ensure that
participants were fixated in the center of the screen. If the
participant was not fixated within a 1° ⫻ 1° bounding box at the
center of the image when they pressed the gamepad button to
initiate a trial, the trial was recycled and did not initiate. Thus,
the participant was always fixating the center of the screen
when they initiated a trial. After a 48-ms delay, the target image
was flashed for 24 ms, and following the prescribed interstimulus interval (ISI) of 0, 71, 165, 259, or 353 ms (which produced
the target-to-mask SOAs of 24, 94, 188, 282, or 376 ms,2 or a
no-mask condition), the mask was presented for 24 ms. It was
predicted that the longest masking SOA, which is only slightly
longer than the average fixation durations on scene images (330
ms), would be equivalent to the no-mask condition. This is
because the retinal image in the no-mask condition would be
masked by a saccade (Irwin, 1992) after, on average, 330 ms.
The shortest SOA was based on the shortest stimulus duration
that produced above-chance performance with this task and
stimuli based on pilot testing. The other SOAs were chosen to
provide roughly equal steps between the two extremes. Following the mask presentation, there was a 750-ms blank screen, and
then a category label was presented until the participant responded using a handheld gamepad. The category label was a
valid description of the scene on 50% of trials (and invalid on
the other 50%). If the label was valid, participants were instructed to press the “yes” button on their game pad, and
otherwise to press the “no” button. The presentation of window
and scotoma scene image conditions were randomized throughout the experiment. All scene image categories appeared
equally often, in random order. All category labels appeared
equally often, and invalid labels were randomly selected from
the remaining nine categories without replacement.
Results
Precursors. Due to poor task performance, we eliminated
data from participants whose average accuracy was at or below the
fifth percentile (⬍51.98% correct; two participants). The fixation
data for each participant was then filtered spatially and temporally
to ensure that the point of fixation was within the 1° ⫻ 1°
2
The monitor’s 85-Hz refresh rate allowed images to be presented for
multiples of 11.76 ms. SOAs were calculated based on this refresh rate,
with the reported SOAs rounded to the nearest whole number.
LARSON, FREEMAN, RINGER, AND LOSCHKY
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
6
Figure 2.
Trial schematic. Both window and scotoma conditions are illustrated for comparison.
bounding box at the center of the image for the entire time period
from the onset of the target to the offset of the mask, and that there
was only a single eye fixation during this time period. Any trials
that did not meet these criteria were discarded. Overall, 17.1% of
the trials were removed from the analysis, resulting in a total of
11,337 trials that satisfied the experimental constraints. A greater
proportion of trials were eliminated from the 376-ms SOA (22% of
these trials were removed; 1,654 trials satisfied the experimental
constraints) and the 282-ms SOA (16% of the trials were removed;
1,799 trials satisfied the experimental constraints) compared with
the remaining SOAs (⬍11% of the trials were removed; 1,919 to
1,993 trials satisfied the experimental constraints per SOA condition). This was due to the fact that viewers were more likely to
spontaneously make an eye movement within the 282-ms and
376-ms SOA conditions than at the shorter SOAs (⬍188-ms
SOA).
Main analyses. As assumed by use of the critical radius,
basic-level scene categorization performance did not differ between the window and scotoma conditions in the no-mask condition, t(53) ⫽ 0.57, p ⫽ .57, Cohen’s d ⫽ .07. This equivalence was
shown by calculating the reciprocal of the JZS Bayes factor (⫽
0.125) from the t value, which showed substantial evidence in
favor of the null (Wetzels et al., 2011).3 Thus, crucially important
for our method, the critical radius produced equal performance in
the window and scotoma conditions when processing time lasted
for a single eye fixation (i.e., when there no mask, and thus the
subject’s next eye movement masked their retinal image of the
scene).
The remaining data were analyzed with a 2 (viewing condition:
window vs. scotoma) ⫻ 5 (SOA: 24, 94, 188, 282, and 376)
within-subjects factorial ANOVA, and a trend analysis was performed to determine if there were any differences in the psychophysical functions between the two viewing conditions over time.
Trend analyses are only reported for interval scaled independent
variables with more than two levels. The results of window and
scotoma performance across SOAs are shown in Figure 3. As
expected, scene categorization performance increased with processing time, as shown by a large and significant main effect of
3
The JZS Bayes Factor can be used to determine the degree of support
for the null hypothesis (Rouder, Speckman, Sun, Morey, & Iverson, 2009).
Wetzels et al. (2011, Table 1) gives interpretations of 1/(JZS Bayes Factor)
as calculated by Rouder et al. (2009). The null hypothesis is stated below
as H0, and the alternative hypothesis is stated as HA. Those values and
interpretations are as follows: A 1/Bayes factor ⬎100 ⫽ decisive evidence
for HA; 30 –100 ⫽ very strong evidence for HA; 10 –30 ⫽ strong evidence
for HA; 3–10 ⫽ substantial evidence for HA; 1–3 ⫽ anecdotal evidence for
HA; 1 ⫽ no evidence; 1/3–1 ⫽ anecdotal evidence for H0; 1/10 –1/3 ⫽
substantial evidence for H0; 1/30 –1/10 ⫽ strong evidence for H0; 1/100 –
1/30 ⫽ very strong evidence for H0; ⬍1/100 ⫽ decisive evidence for H0.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
SPATIOTEMPORAL SCENE GIST DYNAMICS
7
Figure 3. Scene gist accuracy as a function of image type (window vs. scotoma) and processing time (SOA in
ms). Error bars represent the standard error for each data point. No-mask is the unmasked condition.
processing time, F(4, 212) ⫽ 77.40, p ⬍ .001, Cohen’s f ⫽ .752,4
as well as significant linear trend, F(1, 53) ⫽ 257.16, p ⬍ .001.
There was no main effect detected in the window versus scotoma
comparison, F(1, 53) ⫽ 1.53, p ⫽ .22, Cohen’s f ⫽ .031.
Our chief interest was whether or not there would be a significant interaction between the window/scotoma viewing conditions
and processing time. As shown in Figure 3, there was a significant
interaction, F(4, 212) ⫽ 4.05, p ⫽ .003, Cohen’s f ⫽ .150, such
that there was an advantage for the window conditions over the
scotoma conditions, but only at the shortest SOA.5 This was
verified with multiple Bonferroni-corrected t tests (critical p
value ⫽ .01), which compared scene categorization performance
between the window and scotoma conditions at each SOA (24 ms:
t[53] ⫽ 3.19, p ⫽ .002, Cohen’s d ⫽ 0.45; all other ts ⬍ 1.82, ps ⬎
.07). This is consistent with the hypothesis that processing of scene
gist begins in central vision and expands outward over time. As
predicted based on our use of the critical radius stimuli, the longest
SOA (376 ms) produced identical performance between window
(M ⫽ 82.1%, SD ⫽ .10) and scotoma conditions (M ⫽ 82.6%,
SD ⫽ .12), t(53) ⫽ 0.34, p ⫽ .74, Cohen’s d ⫽ 0.04, JZS Bayes
Factor ⫽ 8.86. The longest SOA (M ⫽ 82.4%, SD ⫽ .09) also
produced identical results to the no-mask condition (M ⫽ 82.8%,
SD ⫽ .07), t(53) ⫽ 0.33, p ⫽ .74, Cohen’s d ⫽ 0.03, JZS Bayes
Factor ⫽ 8.89, which was necessarily predicted by our assumption
that a 376-ms masking SOA is equivalent to the complete processing time for a single fixation afforded by the no-mask condition.
Discussion
The results of Experiment 1 showed that the window condition
produced moderately and significantly better scene categorization
performance than the scotoma condition at the earliest processing
time (24-ms SOA). This advantage was gone by the 94-ms SOA,
and thereafter processing was equivalent between both conditions.
These results are inconsistent with our hypothesis based on the
peripheral vision transmission speed advantage, which predicted
that the scotoma condition would be better than the window
condition at the early stages of processing. Likewise, these results
are inconsistent with the hypothesis that the basic-level scene
category can be recognized in the near absence of attention in
parallel across the field of view (Fei-Fei et al., 2005; Li et al.,
2002; Otsuka & Kawaguchi, 2007; Rousselet et al., 2002). However, contrary to the suggestion of van Diepen and d’Ydewalle
(2003), these results are consistent with the sequential attention
model (Henderson, 1992, 1993) as applied to scene gist recognition. This assumes that attention starts at the point of fixation at the
beginning of a fixation, and later extends to the next saccade target
in the visual periphery. The data are also consistent with a combination of (a) the sequential attention model, and (b) a spatiotemporally constrained version of the zoom lens model of attention
(Eriksen & St. James, 1986; Eriksen & Yeh, 1985), in which
attention zooms out from the center of vision over the course of a
fixation, which we call the zoom-out hypothesis. Thus, at the
beginning of the stimulus presentation (24-ms SOA), participants
were more accurate when information was presented at the center
of vision than if it was presented in the visual periphery (beyond
5.54° eccentricity in the scotoma condition), suggesting that attention was focused near the center of vision, in which there was
information in the window condition but not the scotoma condi4
Cohen’s f magnitudes for small, medium, and large effect sizes are
generally given as .10, .25, and .40, respectively (Cohen, 1988).
5
The trend analyses confirmed the results of the ANOVA by showing
that the interaction was significant as a linear trend, F(1, 53) ⫽ 7.09, p ⫽
.010. As this suggests, and as shown in Figure 3, as processing time
increased, performance increased at different rates for the window and
scotoma scene images.
LARSON, FREEMAN, RINGER, AND LOSCHKY
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
8
tion. However, after an additional 70-ms processing time (94-ms
SOA), information presented either centrally or peripherally produced equal performance, suggesting that, by that time, attention
had expanded to encompass the entire image in both the window
and scotoma conditions. Thereafter, performance monotonically
increased at an equivalent rate in both conditions as processing
time increased further.
It is worth noting that asymptotic scene categorization performance was not reached until roughly the 282-ms SOA, which is
considerably longer than in most studies assessing basic-level
scene categorization using visual masking to control processing
time. A simple explanation for this is that both the window and
scotoma conditions were missing scene information that would
otherwise be present in a whole scene image, and thus extra
processing time was required to reach asymptotic performance.
The fact that a whole image condition requires less processing time
points to the importance of processing across the entire visual field
(or at least the entire image), and thus the advantage for central
information early in a fixation should be considered in relative
rather than absolute terms.
Experiment 2
Experiment 1 assessed the spatiotemporal dynamics of scene
gist acquisition, operationalized in terms of basic-level scene categorization, between 24 and 376 ms processing time. However, the
data from Experiment 1 showed that the important sceneprocessing differences between central and peripheral vision were
in the first 100 ms of processing. The advantage for information
presented centrally at 24-ms SOA disappeared by 94 ms, and
thereafter performance was similar across all SOAs up to and
including 376 ms. Thus, a key question is what happens to the
spatiotemporal dynamics of scene gist acquisition during the first
100 ms of viewing a scene? Specifically, when does information
presented to central and peripheral vision become equally useful?
Experiment 2 addressed these questions.
Method
Participants. There were 85 participants (48 females), whose
ages ranged from 18 to 32 years old (M ⫽ 19.51, SD ⫽ 2.05). All
had normal or corrected-to-normal vision (20/30 or better), gave
their institutional review board-approved informed written consent, and received course credit for participating.
Design. The design of Experiment 2 was the same as Experiment 1 except that the SOAs in Experiment 2 were more densely
sampled from the first 100 ms of processing, when meaningful
differences in Experiment 1 were observed. As in Experiment 1,
target and mask images were presented for 24 ms. However, the
ISIs were 0, 12, 24, 47, 71, and 353 ms (producing SOAs of 24, 35,
47, 71, 94, and 376 ms). Because no difference was found between
the 376-ms SOA and the no-mask condition in Experiment 1, the
376-ms SOA served the same function as a no-mask condition in
Experiment 2 (i.e., providing a single eye fixation’s processing
time). Thus, the SOAs used in Experiment 2 reflect a range of
processing time from the minimum processing time necessary for
above chance performance (24-ms SOA) to the point in processing
in Experiment 1 when information presented to both central and
peripheral vision first produced equal performance (94-ms SOA).
The other SOAs were chosen to provide roughly equal steps
between the two extremes. As in Experiment 1, participants completed familiarization and practice trials before the recorded trials.
Stimuli and procedures. All stimuli and procedures were the
same as in Experiment 1, except for the different SOAs.
Results
Precursors. Data cleaning procedures were the same as in
Experiment 1. Due to poor task performance, we eliminated
data from participants whose average accuracy was at or below
the fifth percentile (⬍55.96% correct; four participants). The
fixation data for each subject was then filtered spatially and
temporally to ensure that the point of fixation was within the
1° ⫻ 1° bounding box at the center of the image for the entire
time period from the onset of the target to the offset of the
mask, and that there was only a single eye fixation during this
time period. Any trials that did not meet these criteria were
discarded. This resulted in a total of 17,444 trials that satisfied
the experimental conditions after removing 15.5% of the trials
from the analysis. Similar to Experiment 1, a greater proportion
of data was eliminated from the 376-ms SOA (19% of the trials
were removed; 2,541 trials satisfied the experimental constraints) than the remaining SOAs (⬍12% of the trials were
removed per SOA condition; 2,932 to 3,011 trials satisfied the
experimental constraints per SOA condition).
Main analyses. As assumed by the use of the critical radius,
and seen in Table 1, scene categorization performance in the
window and scotoma conditions was not different at the longest
SOA (376 ms), t(80) ⫽ 0.81, p ⫽ .42, Cohen’s d ⫽ .08, with the
JZS Bayes Factor (⫽ 0.121) indicating substantial evidence in
favor of the null (Wetzels et al., 2011). Thus, when given the
equivalent of a single eye fixation to process the scenes, equal
performance was found in the two viewing conditions.
A 2 (viewing condition: window vs. scotoma) ⫻ 5 (SOA: 24,
35, 47, 71, and 94 ms) within-subjects factorial ANOVA was
used to analyze performance between the two viewing conditions over the first 100 ms of scene processing. As shown in
Figure 4, as expected, basic-level scene categorization increased with processing time, F(4, 320) ⫽ 7.02, p ⬍ .001,
Table 1
Descriptive Statistics for Window and Scotoma Conditions in
Experiment 2 at Each Level of Processing Time
Window
Scotoma
Effect
Processing time
(SOA in ms)
M
SD
M
SD
M
SE
24
35
47
71
94
376
0.668
0.643
0.636
0.658
0.691
0.793
0.109
0.108
0.123
0.113
0.122
0.133
0.634
0.589
0.607
0.623
0.654
0.807
0.119
0.112
0.120
0.136
0.119
0.103
0.032
0.053
0.029
0.035
0.036
⫺0.013
0.016
0.015
0.019
0.018
0.019
0.016
Note. Performance at each processing time (SOA) ⬍ 100 ms for both
window and scotoma conditions was significantly different from performance at the longest SOA (376 ms; the control condition), all ts ⬎ 5.77, all
ps ⬍ .001 (Bonferroni corrected ␣ ⫽ .005). The “Effect” column is the
mean difference between the Window condition and Scotoma condition for
a given level of SOA. SOA ⫽ stimulus onset asynchronies.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
SPATIOTEMPORAL SCENE GIST DYNAMICS
9
Figure 4. Scene gist accuracy as a function of image type (window vs. scotoma) and processing time (SOA in
ms). Error bars represent the standard error for each data point.
Cohen’s f ⫽ .172. Of greater interest, however, the window
condition produced better scene categorization performance
than the scotoma condition, F(1, 80) ⫽ 23.69, p ⬍ .001,
Cohen’s f ⫽ 167. However, the interaction between viewing
condition and processing time did not affect scene categorization accuracy, F(4, 320) ⫽ 0.27, p ⫽ .90.6 The lack of an
interaction indicates that the advantage for the window image
condition over the scotoma image was present over the entire
first 100 ms of processing.
This result is consistent with both reading and scene perception research showing an advantage for information presented
to central vision at the beginning of a fixation (Glaholt et al.,
2012; Rayner et al., 1981, 2003, 2006; van Diepen &
d’Ydewalle, 2003). Additionally, this pattern is consistent with
the conclusion drawn from Experiment 1 that the scene information presented in central vision is used earlier than the
information presented in the visual periphery.
Discussion
The results of Experiment 2 suggest that during the first 100
ms of scene viewing, information presented in central vision
produced better scene gist recognition than that presented in peripheral vision. The results of Experiment 1 showed that performance
for scene information presented in either central or peripheral
vision was equal by the 94-ms SOA, however Experiment 2
showed that the central vision advantage was in fact present
over the entire first 100 ms. This finding is consistent with
previous research showing that in the early processing stages of
an eye fixation, information processed by central vision is
important for reading (Rayner et al., 1981, 2003, 2006), visual
search in scenes, and scene memory (Glaholt et al., 2012; van
Diepen & d’Ydewalle, 2003), whereas information in peripheral vision becomes increasingly important later in a fixation.
Such results are therefore consistent with the sequential model
of attention and the zoom-out hypothesis as applied to scene
gist recognition, in which focal attention starts centrally and
expands over time to encompass information in peripheral
vision. Both explain the overall better performance for central
vision during the first 100 ms of scene processing and the converging
performance between information presented to central and peripheral
vision over time. Thus, these results are also consistent with the idea
that attention does indeed affect scene categorization and scene gist
over the critically important first 100 ms of processing. Nevertheless,
we draw this conclusion cautiously because we did not directly
manipulate attention in either Experiment 1 or 2. Instead, we manipulated processing time and the availability of task-relevant information as a function of retinal eccentricity. Experiment 3 addressed this
issue by directly manipulating viewers’ attention while they carried
out the scene categorization task in either the window or scotoma
conditions.
Experiment 3
Experiments 1 and 2 suggest an advantage for basic-level
scene categorization in central vision (i.e., the window condition) at early processing times (⬍100-ms SOA); however, this
advantage disappeared for later processing times, resulting in
equivalent scene categorization performance for information
presented in central and peripheral vision. These results are
consistent with the idea that while attempting to recognize the
gist of rapidly presented scenes, attention starts centrally and
expands to the visual periphery over time. This was predicted
6
Trend analyses show that processing time produced a significant linear
trend for accuracy, F(1, 80) ⫽ 6.11, p ⫽ .016. Additionally, the interaction
between processing time and window/scotoma conditions on accuracy
showed no evidence of a linear trend, F(1, 80) ⫽ .04, p ⫽ .84.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
10
LARSON, FREEMAN, RINGER, AND LOSCHKY
by our proposed zoom-out hypothesis, which derives from a
combination of the sequential and zoom lens models of attention. However, Experiments 1 and 2 did not explicitly manipulate attention. Thus, Experiment 3 did just that, explicitly
manipulating attention in order to test whether attentional allocation underlies the changing spatiotemporal patterns of scene
categorization performance found in Experiments 1 and 2.
Specifically, if the early advantage for the window condition is
due to attention starting in central vision at the beginning of the
first fixation, then that advantage should be eliminated if it is
possible to strategically allocate attention to the visual periphery in the scotoma condition.
Attention was manipulated to be either focused in central vision or
spread out to peripheral vision through a probability manipulation of
the type often used in studies of covert attention (Geng & Behrmann,
2005; Yantis & Egeth, 1999; Yantis & Jonides, 1984). Such studies
have shown that attention tends to be allocated to the area most likely
to contain target information. Thus, we manipulated the proportion of
trials in which either the window or the scotoma conditions were
presented. In Experiments 1 and 2, the window and scotoma trials
occurred randomly with equal probability. However, in Experiment 3,
participants were assigned either to (a) the window-dominant condition, in which 80% of the trials presented window stimuli, and
scotoma images composed the remaining 20%; or (b) the scotomadominant condition, in which these proportions are reversed.
Using this probability-based attentional manipulation, we can
determine whether spatial attention modulates the spatiotemporal patterns of scene categorization performance observed in
Experiments 1 and 2. If the early central vision advantage is in
fact the result of an attentional process, then the early central
advantage should remain in the window-dominant condition,
but should be completely eliminated in the scotoma-dominant
condition, because attention would be strategically spread
across both central and peripheral vision.
Importantly, this logic should also take into account the fact
that, given sufficient processing time, performance should be equal
in both window and scotoma conditions, due to our use of the
critical radius. Thus, the critical tests between the proposed alternative explanations must be at the shortest SOAs (e.g., 35-ms
SOA), for which any spatiotemporal effects of attention on scene
gist recognition would be expected to be greatest.
participants completed 320 experimental trials (equivalent to the total
number of images), of which a randomly selected 80% of trials (i.e.,
256) were presented in the window condition, with the remaining
20% of trials (i.e., 64) presented in the scotoma condition. In the 80%
scotoma condition, the ratio of window to scotoma trials was reversed. Participants were informed of the probabilities of viewing a
window or scotoma scene for their respective condition. Additionally,
in order to prepare participants for their attention condition, the 32
practice trials had the same ratio of window to scotoma trials as their
condition (i.e., 26 images in the dominant condition and the remaining
six images from the other condition).
Results
Precursors. Data cleaning procedures were the same as in
Experiments 1 and 2. Due to poor task performance, we eliminated
data from participants whose average accuracy was at or below the
fifth percentile (⬍60.4% correct; six participants). The fixation
data for each subject was then filtered spatially and temporally to
ensure that the point of fixation was within the 1° ⫻ 1° bounding
box at the center of the image for the entire time period from the
onset of the target to the offset of the mask, and that there was only
a single eye fixation during this time period. Any trials that did not
meet these criteria were discarded. Data cleaning resulted in a total
of 29,276 trials that were included in the analysis, after deleting
19.0% of the trials. As in Experiments 1 and 2, a greater proportion
of trials were eliminated from the 376-ms SOA (23% of the trials
were removed; 6,525 trials satisfied the experimental constraints)
compared with the remaining SOAs (⬍11% of the trials were
removed per SOA condition; 7,538 to 7,612 trials satisfied the
experimental constraints per SOA condition).
Main analyses. As assumed from our use of the critical radius,
scene categorization performance did not differ between the window
and scotoma viewing conditions at 376-ms SOA, in either the 80%
window, t(48) ⫽ 1.44, p ⫽ .16, Cohen’s d ⫽ .17 JZS Bayes Factor ⫽
3.30, or 80% scotoma conditions, t(56) ⫽ 0.33, p ⫽ .74, Cohen’s d ⫽
.04, JZS Bayes Factor ⫽ 9.12, as shown in Table 2, with both JZS
Table 2
Descriptive Statistics for Window and Scotoma Scenes at Each
Level of Processing for the Window- and Scotoma-Dominant
Attentional Conditions in Experiment 3
Method
Participants. There were 112 participants (80 females) with
ages ranging from 17 to 36 (M ⫽ 19.5, SD ⫽ 2.79). All had normal
or corrected-to-normal vision (20/30 acuity or better), gave their
institutional review board-approved informed written consent
(with written parental consent also given for those under the age of
18), and received course credit for participating.
Design. The experiment used a mixed design: 2 (window vs.
scotoma) ⫻ 4 (processing time: 35-, 70-, 105-, 376-ms SOA), both
within-subjects, ⫻ 2 (attentional bias: 80% window vs. 80%
scotoma) between subjects, with participants randomly assigned to
the attentional bias conditions.
Stimuli. An additional eight images per basic-level scene category were added to the image set used in Experiments 1 and 2.
Procedure. Procedures were the same as in Experiments 1 and
2, with the following exceptions. In the 80% window condition,
Processing time
(SOA in ms)
Window-dominant
condition
35
71
106
376
Scotoma-dominant
condition
35
71
106
376
Window
Scotoma
Effect
M
SD
M
SD
M
SE
0.692
0.674
0.715
0.796
0.073
0.074
0.078
0.066
0.593
0.646
0.666
0.768
0.124
0.111
0.153
0.146
0.099
0.028
0.049
0.027
0.020
0.018
0.023
0.018
0.644
0.686
0.698
0.825
0.131
0.142
0.132
0.114
0.631
0.659
0.719
0.830
0.059
0.070
0.083
0.068
0.012
0.026
⫺0.021
⫺0.005
0.018
0.018
0.017
0.017
Note. The “Effect” column is the mean difference between the window
condition and scotoma condition for a given level of SOA. SOA ⫽
stimulus onset asynchronies.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
SPATIOTEMPORAL SCENE GIST DYNAMICS
Bayes Factors providing good evidence for the null. As before, this
equivalence between window and scotoma conditions, when given
the equivalent of one eye fixation’s processing time, means that any
difference observed between these two conditions at earlier processing times cannot be the result of differences in viewable information
available to each. Rather, these differences must be due to spatiotemporal differences in processing.
The remaining data were submitted to a 2 (attentional manipulation: window- vs. scotoma-dominant conditions) ⫻ 2 (viewing
condition: window vs. scotoma image) ⫻ 3 (SOA: 35, 71, 106)
mixed factorial ANOVA. As shown in Figure 5, scene categorization increased with processing time (SOA), F(2, 208) ⫽ 18.39,
p ⬍ .001, Cohen’s f ⫽ .165.7 Of greater interest, however, and
consistent with Experiments 1 and 2, the window conditions produced better categorization performance than the scotoma conditions, as evidenced by a significant main effect of viewing condition, F(1, 104) ⫽ 17.83, p ⬍ .001, Cohen’s f ⫽ .115. There was no
main effect of the attentional manipulation on scene categorization
accuracy, F(1, 104) ⫽ 0.62, p ⫽ .433. Likewise, there was no
Viewing Condition ⫻ Processing Time interaction, F(2, 208) ⫽
2.30, p ⫽ .10, Cohen’s f ⫽ .045.8
The Attentional Manipulation ⫻ Processing Time interaction
was not significant, F(2, 208) ⫽ 0.78, p ⫽ .46, nor were there any
significant trends, ps ⱖ .24. However, of critical importance was
the significant Attentional Manipulation ⫻ Viewing Condition
interaction, F(1, 104) ⫽ 11.71, p ⫽ .001, Cohen’s f ⫽ .009,
namely, that the difference between the window and scotoma
viewing conditions was affected by the probability-based manipulation of spatial attention. In the window-dominant condition,
scene categorization performance was better for the window images (M ⫽ .69, SD ⫽ 0.05) than the scotoma images (M ⫽ .64,
SD ⫽ 0.08), t(48) ⫽ 4.93, p ⬍ .001, Cohen’s d ⫽ 0.53, but this
advantage was eliminated in the scotoma-dominant condition
(window: M ⫽ .68, SD ⫽ 0.08; scotoma: M ⫽ .67, SD ⫽ 0.05),
t(56) ⫽ 0.55, p ⫽ .59, Cohen’s d ⫽ 0.11, JZS Bayes Factor ⫽
0.121, providing substantial support for the null. Thus, the early
advantage for scene gist processing in central vision was alternatively eliminated or maintained by the allocation of spatial attention.
The three-way interaction between attentional manipulation,
viewing condition, and processing time (SOA) failed to reach
significance, F(2, 208) ⫽ 2.56, p ⫽ .08, Cohen’s f ⫽ .049,
suggesting that the two-way interaction between attentional manipulation and viewing condition did not differ over the first 100
ms of processing. However, a trend analysis showed a significant
quadratic trend for the three-way interaction, F(1, 104) ⫽ 5.16,
p ⫽ .025, which may be explained by visual inspection of Figure
5. Figure 5 shows that the window advantage in the windowdominant condition is greatest at the shortest SOA, whereas in the
scotoma-dominant condition, there is no difference between window and scotoma conditions at any SOA. This observation was
tested with a pair of 2 (viewing condition: window vs. scotoma
image) ⫻ 3 (processing time: 35-, 71-, 106-ms SOA) withinsubjects factorial ANOVAs for each respective attentional manipulation condition. The window-dominant condition had significant
main effects for both processing time, F(2, 96) ⫽ 5.54, p ⫽ .005,
Cohen’s f ⫽ .18, and viewing condition, F(1, 48) ⫽ 23.88, p ⬍
.001, Cohen’s f ⫽ .28, and a marginally significant interaction
between processing time and the viewing condition, F(2, 96) ⫽
11
3.06, p ⫽ .052, Cohen’s f ⫽ .118, due to the greatest difference
being at the 35-ms SOA. Conversely, the scotoma-dominant condition only showed a main effect of processing time, F(2, 112) ⫽
14.36, p ⬍ .001, Cohen’s f ⫽ .28, but no main effect for viewing
condition, F(1, 56) ⫽ .39, p ⫽ .53, nor a significant Processing
Time ⫻ Viewing Condition interaction, F(2, 112) ⫽ 1.67, p ⫽ .19,
Cohen’s f ⫽ .063. These results are consistent with the hypothesis
that the observed spatiotemporal differences in scene categorization in Experiments 1 and 2 were due to differential allocation of
attention over time and space. Specifically, the current results
showed that the early central vision advantage is modulated by
attention, such that it was completely eliminated if covert attention
was strategically reallocated over the entire visual field, as shown
in the scotoma-dominant condition.
Discussion
Experiment 3 examined whether the spatiotemporal pattern
of scene gist acquisition, as seen in Experiments 1 and 2, could
be reasonably explained in terms of an attentional mechanism.
If so, then manipulating the spatial distribution of attention
could potentially eliminate the central advantage by strategically reallocating some of the attentional resources from central
vision to peripheral vision. Thus, attention would be allocated
over the entire visual field, resulting in equivalent performance
between these regions. To test this explanation, Experiment 3
used a probability-based attentional manipulation in which either centrally or peripherally presented scene information was
presented 80% of the time. The results from Experiment 3
showed that the central advantage was indeed eliminated when
scene information was more likely to be presented in the visual
periphery in the scotoma-dominant condition. This suggests
that attention was reallocated based on the likely spatial location of scene information. This, in turn, is consistent with the
hypothesis that the spatiotemporal pattern of scene categorization performance observed in Experiments 1 and 2 can be
explained in terms of differential allocation of covert attention—namely, the early scene categorization advantage for central vision, was either maintained or eliminated by differential
attentional allocation.
The results also show that observers can draw on attention
instructions (emphasizing central vs. peripheral processing) to
impact performance very early in visual processing. Early literature demonstrated volitional flexibility in directing attention
to specified pictures during rapid serial visual presentations
(Intraub, 1984). The current study shows a similar volitional
flexibility in attention to different spatial areas during presentation of a single image.
General Discussion
The current study examined the spatiotemporal dynamics of
scene gist recognition, namely, the rapid acquisition of a semantic representation of a scene within a single eye fixation.
7
A significant linear trend was found for processing time on scene gist
accuracy, F(1, 104) ⫽ 35.74, p ⬍ .001.
8
The trend analysis did show a significant linear interaction between
viewing condition and processing time, F(1, 104) ⫽ 4.25, p ⫽ .042.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
12
LARSON, FREEMAN, RINGER, AND LOSCHKY
Figure 5. Experiment 3 results showing the window-dominant condition and the scotoma-dominant condition.
Error bars represent the standard error for each data point.
The fact that scene gist acquisition occurs within a single
fixation— during which time all elements of the retinal image
have fixed locations—led us to investigate whether different
regions of the visual field, specifically, central versus peripheral vision, contribute differentially to this process over the
course of the critical first fixation on a scene. The three experiments in the current study strongly suggest that during the first
fixation on a scene, processing of the gist of a scene is influenced by covert attention, which begins focused in central
vision and rapidly expands outward into the visual periphery
within the first 100 ms of viewing.
Over the past decade, there has been an ongoing debate regarding the role of attention in scene gist recognition. Several previous
studies have shown that rapid scene categorization can occur in the
near absence of attention for peripheral scenes when attention is
focused centrally (Fei-Fei et al., 2005; Li et al., 2002), or multiple
scenes in parallel across the field of view (Rousselet et al., 2002).
Such findings suggest that attention has little, if any, effect on
recognizing scene gist and that both central and peripheral scene
information should be equally useful for recognizing scene gist
over the course of a single fixation. Conversely, several more
recent studies have challenged these conclusions by showing that
rapid scene categorization is affected by attentional limits (Cohen
et al., 2011; Evans & Treisman, 2005; Walker et al., 2008) and that
there are limits to the parallel processing of scene gist (Rousselet,
Thorpe, & Fabre-Thorpe, 2004).
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
SPATIOTEMPORAL SCENE GIST DYNAMICS
Importantly, whether one can identify multiple scenes simultaneously in their visual periphery, or be able to shift their locus
of attention from central vision to peripheral vision within a
single image, might involve different aspects of attention. The
current study investigated changes in processing within a single
scene over time. Experiments 1 and 2 showed an asymmetry in
processing across time and space that favored central vision
early in a fixation, which is inconsistent with the claim that
scene gist is processed homogeneously across the field of view
by preattentive processes over the course of a single fixation.
Furthermore, Experiment 3 provided evidence consistent with
the hypothesis that the processing asymmetry observed in Experiments 1 and 2 was due to the spatiotemporal dynamics of
attentional allocation over the course of a fixation, by showing
that spreading attention into the visual periphery eliminated the
central advantage, whereas focusing attention in the center of
vision maintained the central advantage.
Indeed, the condition least likely to be attended—the scotoma
condition at the shortest SOA when information was expected
to be presented in a central window—produced the worst performance. Nevertheless, although scene categorization in this
condition was extremely poor (.59 accuracy), it was significantly above chance (.50), t(48) ⫽ 5.25, p ⬍ .001, Cohen’s d ⫽
1.52, and thus the results of the current study do not show that
attention is necessary for scene gist recognition. It should be
noted, however, that the attention manipulation in Experiment 3
involved divided attention, rather than inattention, and thus is
not a rigorous test of the necessity of attention for scene gist
recognition (Cohen et al., 2011; Mack & Rock, 1998) and
leaves ample room for future investigation of the role of attention in scene gist processing. The above-chance performance in
the condition least likely to be attended is also consistent with
the hypothesis that on certain trials, for certain participants,
attention was at least moderately allocated to the periphery,
thus producing performance slightly, though significantly,
above chance.
The current study also sheds light on the role of peripheral
vision in scene gist recognition. Previous studies have shown
the importance of information from peripheral vision for rapid
scene-processing tasks, including animal detection and scene
gist recognition (Boucart et al., 2013; Larson & Loschky, 2009;
Thorpe et al., 2001; Tran, Rambaud, Despretz, & Boucart,
2010). Furthermore, physiological evidence shows that information from peripheral vision, transmitted primarily by the
magnocellular pathway, activates early cortical visual areas
prior to information from central vision, primarily from the
parvocellular pathway (Carrasco et al., 2003; Livingstone &
Hubel, 1988; Nowak et al., 1995). Together, such findings
suggest that scene information from peripheral vision may be
used earlier for scene gist recognition than information from
central vision. However, in the current study, although peripheral vision provided sufficient information for scene gist recognition, that information was used relatively later in a fixation
than information from central vision. A question for further
studies is why the processing speed advantage for peripheral
vision via the magnocellular pathway does not translate into
earlier processing of information for scene gist recognition
from peripheral vision.
13
The results of the current study most strongly support the
zoom-out hypothesis, which states that during gist recognition,
attention proceeds from central vision to peripheral vision over
the course of the first fixation on a scene. Other research has
investigated the effects on various scene perception tasks of
masking either central vision or peripheral vision early in a
fixation, and has shown that masking central vision has greater
effects on visual search and memory for scenes during the first
50 ms of fixations, whereas masking peripheral vision continues to have effects up through 70 to 100 ms into a fixation
(Glaholt et al., 2012; van Diepen & d’Ydewalle, 2003). Those
results suggest that scene information in central vision is extracted earlier in a fixation than is information in peripheral
vision (Glaholt et al., 2012; van Diepen & d’Ydewalle, 2003).
Importantly, the current study contradicts a prediction proposed
by van Diepen and d’Ydewalle (2003), namely, that scene
identification (gist recognition) would not follow the sequential
attention model, but instead would be immediately more biased
to peripheral vision. Instead, the current results are consistent
with the idea that a wide array of visual tasks, ranging from
reading, to visual search, to scene memory, to scene gist recognition, all begin with attention located in central vision,
which then proceeds outward into peripheral vision over the
first 100 ms of a fixation.
The results of the current study and those of van Diepen and
d’Ydewalle (2003) and Glaholt et al. (2012) are consistent with
the zoom-out hypothesis, which derives from both the sequential attention model for eye-movements (Henderson, 1992,
1993) and the zoom-lens model of attention (Eriksen & St.
James, 1986; Eriksen & Yeh, 1985; Müller, Bartelt, Donner,
Villringer, & Brandt, 2003; Seiple, Clemens, Greenstein, Holopigian, & Zhang, 2002). The sequential attention model argues
that attention starts focused on the foveal object at the beginning of each fixation and then moves to a parafoveal or peripheral target object before the end of the fixation, and is thus an
object-based theory of attention. The zoom-lens model argues
that attention can vary from highly focused at the center of
vision to broadly diffuse from central to peripheral vision, and
is thus a space-based theory of attention. The zoom-out hypothesis argues that scene gist processing proceeds from central
vision to peripheral vision over the course of a single fixation,
and thus is essentially a space-based account. However, the
current study cannot speak to whether the spread of attention
outward from the center of vision is spatially relatively uniform
or instead is nonuniform and object-based. This is a critical
question for future research.
It could be argued that the observed spatiotemporal processing differences in scene categorization in Experiments 1
through 3 could be due to content differences between the
central and peripheral image regions of our photographic stimuli. Although possible, this seems highly unlikely for the following reasons. First, we used a critical radius that perfectly
divided scene images into central and peripheral regions that
produced equal performance when presented for the duration of
a single fixation. The results of Experiments 1 through 3
confirmed this assumption of equality after processing for a
single fixation, with the central advantage only occurring during the first 100 ms of a fixation. Thus, any content advantage
for central vision would have to occur only early in a fixation.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
14
LARSON, FREEMAN, RINGER, AND LOSCHKY
Second, Experiment 3 showed that the central advantage was
eliminated when attention was strategically allocated to the
visual periphery. The latter finding is hard to reconcile with
claims that the central advantage is due to differences in content, but is consistent with the zoom-out hypothesis.9
However, an important question for future research is which
information is used in central vision and peripheral vision
earlier and later in a fixation. Such studies should take into
account various well-known differences in processing between
central and peripheral vision. For example, central vision is
more sensitive than peripheral vision to higher spatial frequencies (Banks, Sekuler, & Anderson, 1991; Cannon, 1985;
Loschky, McConkie, Yang, & Miller, 2005; Peli & Geri, 2001;
Pointer & Hess, 1989), color information (Mullen & Losada,
1999; Nagy & Wolf, 1993; Rovamo & Iivanainen, 1991), and
phase information (Knight, Shapiro, & Lu, 2008), among others. Previous studies have already contributed to our understanding of the roles in scene gist recognition of lower versus
higher spatial frequencies (McCotter et al., 2005; Oliva &
Schyns, 1997; Schyns & Oliva, 1994), color information
(Castelhano & Henderson, 2008; Goffaux et al., 2005; Loschky
& Simons, 2004; Oliva & Schyns, 2000; Steeves et al., 2004),
and phase information (Guyader, Chauvin, Peyrin, Hérault, &
Marendaz, 2004; Joubert, Rousselet, Fabre-Thorpe, & Fize,
2009; Loschky et al., 2007, 2010; Loschky & Larson, 2008;
McCotter et al., 2005; Wichmann, Braun, & Gegenfurtner,
2006). Studies that examine the extraction of different types of
scene information from central versus peripheral vision over
time will be critical to understanding how scene representations
are constructed and stored in memory. By knowing which types
of information are processed by central versus peripheral vision, and the time course of their use, we can build computational models of scene categorization that more closely reflect
how humans perceive and understand their visual environment.
Another interesting question for further research is whether
the results shown in the current experiments generalize to more
natural viewing conditions in which the viewer is not required
to fixate the center of the screen prior to the onset of the image.
In natural viewing, the first fixation on a scene is simply
wherever the viewer happens to fixate. Thus, one might argue
that requiring the participants in the current study to fixate the
center of the screen prior to onset of the image could have
enforced the zoom-out pattern of attention that starts from
central vision. This question cannot be answered by the results
of the current studies. However, if requiring viewers to fixate
the center of the screen prior to the onset of an image caused
attention to zoom out, then this must also hold for the vast
majority of previous studies on scene gist recognition, which
have similarly required central fixation.
In sum, the present study suggests that the spatiotemporal
dynamics of attention across the visual field affect viewers’ gist
recognition during the first fixation on a scene. The results
provide strong support for the zoom-out hypothesis, in which
rapid gist extraction from a scene unfolds from central to
peripheral vision via visual selective attention. This provides
important spatiotemporal constraints for theories of the rapid
understanding of real-world scenes.
9
Additionally, previous research suggests that diagnostic objects in
central vision should not be responsible for our reported effects. Specifically, Davenport and Potter (2004) presented scene images with a semantically consistent or inconsistent object presented at the center of vision.
They showed that a semantically inconsistent object resulted in worse
scene categorization performance, but a semantically consistent object did
not facilitate scene categorization compared with the same scene without
the central object. Thus, if central objects affected our results, then only
semantically inconsistent objects should have had such an influence, and
they should have reduced the central advantage we found. However, there
were no semantically inconsistent objects within our scene categories.
References
Bacon-Macé, N., Macé, M. J., Fabre-Thorpe, M., & Thorpe, S. J. (2005).
The time course of visual processing: Backward masking and natural
scene categorisation. Vision Research, 45, 1459 –1469. doi:10.1016/j
.visres.2005.01.004
Banks, M. S., Sekuler, A. B., & Anderson, S. J. (1991). Peripheral spatial
vision: Limits imposed by optics, photoreceptors, and receptor pooling.
Journal of the Optical Society of America, A, Optics, Image & Science,
8, 1775–1787. doi:10.1364/JOSAA.8.001775
Bar, M., & Ullman, S. (1996). Spatial context in recognition. Perception,
25, 343–352. doi:10.1068/pp.250343
Biederman, I., Mezzanotte, R., & Rabinowitz, J. (1982). Scene perception:
Detecting and judging objects undergoing relational violations. Cognitive Psychology, 14, 143–177. doi:10.1016/0010-0285(82)90007-X
Biederman, I., Rabinowitz, J., Glass, A., & Stacy, E. (1974). On the
information extracted from a glance at a scene. Journal of Experimental
Psychology, 103, 597– 600. doi:10.1037/h0037158
Boucart, M., Moroni, C., Thibaut, M., Szaffarczyk, S., & Greene, M.
(2013). Scene categorization at large visual eccentricities. Vision Research, 86, 35– 42. doi:10.1016/j.visres.2013.04.006
Boyce, S. J., & Pollatsek, A. (1992). Identification of objects in scenes: The
role of scene background in object naming. Journal of Experimental
Psychology: Learning, Memory, and Cognition, 18, 531–543. doi:
10.1037/0278-7393.18.3.531
Breitmeyer, B. G., & Ogmen, H. (2006). Visual masking: Time slices
through conscious and unconscious vision. Oxford, UK: Clarendon
Press.
Brewer, W. F., & Treyens, J. C. (1981). Role of schemata in memory for
places. Cognitive Psychology, 13, 207–230. doi:10.1016/00100285(81)90008-6
Calvo, M. G. (2005). Relative contribution of vocabulary knowledge and
working memory span to elaborative inferences in reading. Learning and
Individual Differences, 15, 53– 65. doi:10.1016/j.lindif.2004.07.002
Calvo, M. G., Nummenmaa, L., & Hyona, J. (2008). Emotional scenes in
peripheral vision: Selective orienting and gist processing, but not content
identification. Emotion, 8, 68 – 80. doi:10.1037/1528-3542.8.1.68
Cannon, M. W. (1985). Perceived contrast in the fovea and periphery.
Journal of the Optical Society of America, A, Optics, Image & Science,
2, 1760 –1768. doi:10.1364/JOSAA.2.001760
Carrasco, M., McElree, B., Denisova, K., & Giordano, A. M. (2003). Speed
of visual processing increases with eccentricity. Nature Neuroscience, 6,
699 –700. doi:10.1038/nn1079
Castelhano, M. S., & Henderson, J. M. (2008). The influence of color on
the perception of scene gist. Journal of Experimental Psychology: Human Perception and Performance, 34, 660 – 675. doi:10.1037/00961523.34.3.660
Cohen, J. (1988). Statistical Power for the behavioral sciences (2nd ed.).
Hillsdale, NJ: Erlbaum.
Cohen, M. A., Alvarez, G. A., & Nakayama, K. (2011). Natural-scene
perception requires attention. Psychological Science, 22, 1165–1172.
doi:10.1177/0956797611419168
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
SPATIOTEMPORAL SCENE GIST DYNAMICS
Curcio, C. A., Sloan, K. R., Kalina, R. E., & Hendrickson, A. E. (1990).
Human photoreceptor topography. The Journal of Comparative Neurology, 292, 497–523. doi:10.1002/cne.902920402
Curcio, C. A., Sloan, K. R., Packer, O., Hendrickson, A. E., & Kalina, R. E.
(1987). Distribution of cones in human and monkey retina: Individual
variability and radial asymmetry. Science, 236, 579 –582. doi:10.1126/
science.3576186
Davenport, J. L., & Potter, M. C. (2004). Scene consistency in object and
background perception. Psychological Science, 15, 559 –564. doi:
10.1111/j.0956-7976.2004.00719.x
Eckstein, M. P., Drescher, B. A., & Shimozaki, S. S. (2006). Attentional
cues in real scenes, saccadic targeting, and Bayesian priors. Psychological Science, 17, 973–980. doi:10.1111/j.1467-9280.2006.01815.x
Eriksen, C. W.St., & James, J. D. (1986). Visual attention within and
around the field of focal attention: A zoom lens model. Perception &
Psychophysics, 40, 225–240. doi:10.3758/BF03211502
Eriksen, C. W., & Yeh, Y. Y. (1985). Allocation of attention in the visual
field. Journal of Experimental Psychology: Human Perception and
Performance, 11, 583–597. doi:10.1037/0096-1523.11.5.583
Evans, K. K., & Treisman, A. (2005). Perception of objects in natural
scenes: Is it really attention free? Journal of Experimental Psychology:
Human Perception and Performance, 31, 1476. doi:10.1037/0096-1523
.31.6.1476
Fei-Fei, L., Iyer, A., Koch, C., & Perona, P. (2007). What do we perceive
in a glance of a real-world scene? Journal of Vision, 7, 1–29. doi:
10.1167/7.1.10
Fei-Fei, L., VanRullen, R., Koch, C., & Perona, P. (2005). Why does
natural scene categorization require little attention? Exploring attentional requirements for natural and synthetic stimuli. Visual Cognition,
12, 893–924. doi:10.1080/13506280444000571
Francis, G., & Cho, Y. S. (2008). Effects of temporal integration on the
shape of visual backward masking functions. Journal of Experimental
Psychology: Human Perception and Performance, 34, 1116 –1128. doi:
10.1037/0096-1523.34.5.1116
Geng, J. J., & Behrmann, M. (2005). Spatial probability as an attentional
cue in visual search. Perception & Psychophysics, 67, 1252–1268.
doi:10.3758/BF03193557
Girard, P., & Koenig-Robert, R. (2011). Ultra-rapid categorization of
Fourier-spectrum equalized natural images: Macaques and humans perform similarly. PLoS ONE, 6, e16453. doi:10.1371/journal.pone
.0016453
Glaholt, M. G., Rayner, K., & Reingold, E. M. (2012). The mask-onset
delay paradigm and the availability of central and peripheral visual
information during scene viewing. Journal of Vision, 12(1), 9. doi:
10.1167/12.1.32
Goffaux, V., Jacques, C., Mouraux, A., Oliva, A., Schyns, P. G., &
Rossion, B. (2005). Diagnostic colours contribute to the early stages of
scene categorization: Behavioural and neurophysiological evidence. Visual Cognition, 12, 878 – 892. doi:10.1080/13506280444000562
Gordon, R. D. (2004). Attentional allocation during the perception of
scenes. Journal of Experimental Psychology: Human Perception and
Performance, 30, 760 –777. doi:10.1037/0096-1523.30.4.760
Greene, M. R., & Oliva, A. (2009). The briefest of glances: The time
course of natural scene understanding. Psychological Science, 20, 464 –
472. doi:10.1111/j.1467-9280.2009.02316.x
Guyader, N., Chauvin, A., Peyrin, C., Hérault, J., & Marendaz, C. (2004).
Image phase or amplitude? Rapid scene categorization is an amplitudebased process. Comptes Rendus Biologies, 327, 313–318. doi:10.1016/
j.crvi.2004.02.006
Henderson, J. M. (1992). Visual attention and eye movement control
during reading and picture viewing. In K. Rayner (Ed.), Eye movements
and visual cognition: Scene perception and reading (Vol. 10, pp. 260 –
283). New York, NY: Springer-Verlag. doi:10.1007/978-1-4612-28523_15
15
Henderson, J. M. (1993). Visual attention and saccadic eye movements. In
J. V. R. Gery d’Ydewalle (Ed.), Perception and cognition: Advances in
eye movement research. Studies in visual information processing (Vol.
4, pp. 37–50). Amsterdam, Netherlands: North-Holland/Elsevier Science
Publishers.
Hollingworth, A., & Henderson, J. M. (1998). Does consistent scene
context facilitate object perception? Journal of Experimental Psychology: General, 127, 398 – 415. doi:10.1037/0096-3445.127.4.398
Hollingworth, A., Schrock, G., & Henderson, J. M. (2001). Change detection in the flicker paradigm: The role of fixation position within the
scene. Memory & Cognition, 29, 296 –304. doi:10.3758/BF03194923
Holmes, D. L., Cohen, K. M., Haith, M. M., & Morrison, F. J. (1977).
Peripheral visual processing. Perception & Psychophysics, 22, 571–577.
doi:10.3758/BF03198765
Intraub, H. (1981). Rapid conceptual identification of sequentially presented pictures. Journal of Experimental Psychology: Human Perception and Performance, 7, 604 – 610. doi:10.1037/0096-1523.7.3.604
Intraub, H. (1984). Conceptual masking: The effects of subsequent visual
events on memory for pictures. Journal of Experimental Psychology:
Learning, Memory, and Cognition, 10, 115–125. doi:10.1037/0278-7393
.10.1.115
Irwin, D. E. (1992). Memory for position and identity across eye movements. Journal of Experimental Psychology: Learning, Memory, and
Cognition, 18, 307–317. doi:10.1037/0278-7393.18.2.307
Joubert, O. R., Rousselet, G. A., Fabre-Thorpe, M., & Fize, D. (2009).
Rapid visual categorization of natural scene contexts with equalized
amplitude spectrum and increasing phase noise. Journal of Vision, 9(1),
1–16.
Joubert, O. R., Rousselet, G. A., Fize, D., & Fabre-Thorpe, M. (2007).
Processing scene context: Fast categorization and object interference.
Vision Research, 47, 3286 –3297. doi:10.1016/j.visres.2007.09.013
Kirchner, H., & Thorpe, S. J. (2006). Ultra-rapid object detection with
saccadic eye movements: Visual processing speed revisited. Vision
Research, 46, 1762–1776. doi:10.1016/j.visres.2005.10.002
Knight, E., Shapiro, A., & Lu, Z.-L. (2008). Drastically different percepts
of five illusions in foveal and peripheral vision reveal their differences
in representing visual phase. Journal of Vision, 8, 967. doi:10.1167/8.6
.967
Larson, A. M., & Loschky, L. C. (2009). The contributions of central
versus peripheral vision to scene gist recognition. Journal of Vision,
9(10), 1–16.
Li, F. F., VanRullen, R., Koch, C., & Perona, P. (2002). Rapid natural
scene categorization in the near absence of attention. PNAS Proceedings
of the National Academy of Sciences of the United States of America, 99,
9596 –9601. doi:10.1073/pnas.092277599
Livingstone, M., & Hubel, D. H. (1988). Segregation of form, color,
movement, and depth: Anatomy, physiology, and perception. Science,
240, 740 –749. doi:10.1126/science.3283936
Loschky, L. C., Hansen, B. C., Sethi, A., & Pydimari, T. (2010). The role
of higher-order image statistics in masking scene gist recognition. Attention, Perception, & Psychophysics, 72, 427– 444. doi:10.3758/APP
.72.2.427
Loschky, L. C., & Larson, A. M. (2008). Localized information is necessary for scene categorization, including the natural/man-made distinction. Journal of Vision, 8(1), 1–9.
Loschky, L. C., & Larson, A. M. (2010). The natural/man-made distinction
is made prior to basic-level distinctions in scene gist processing. Visual
Cognition, 18, 513–536. doi:10.1080/13506280902937606
Loschky, L. C., Larson, A. M., Smerchek, S., & Finan, S. (2008). The
superordinate natural/man-made distinction is perceived before basic
level distinctions in scene gist recognition. Journal of Vision, 8(6), 738.
doi:10.1167/8.6.738
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
16
LARSON, FREEMAN, RINGER, AND LOSCHKY
Loschky, L. C., McConkie, G. W., Yang, J., & Miller, M. E. (2005). The
limits of visual resolution in natural scene viewing. Visual Cognition, 12,
1057–1092. doi:10.1080/13506280444000652
Loschky, L. C., Sethi, A., Simons, D. J., Pydimari, T., Ochs, D., &
Corbeille, J. (2007). The importance of information localization in scene
gist recognition. Journal of Experimental Psychology: Human Perception and Performance, 33, 1431–1450. doi:10.1037/0096-1523.33.6
.1431
Loschky, L. C., & Simons, D. J. (2004). The effects of spatial frequency
content and color on scene gist perception [Abstract]. Journal of Vision,
4(8), 881. doi:10.1167/4.8.881
Mack, A., & Rock, I. (1998). Inattentional blindness (Vol. 6). Cambridge,
MA: MIT Press.
Malcolm, G. L., Nuthmann, A., & Schyns, P. G. (2011). Ordinate and
subordinate level categorizations of real-world scenes: An eye movement study. Journal of Vision, 11(11), 1112. doi:10.1167/11.11.1112
McConkie, G. W., & Rayner, K. (1975). The span of the effective stimulus
during a fixation in reading. Perception & Psychophysics, 17, 578 –586.
doi:10.3758/BF03203972
McCotter, M., Gosselin, F., Sowden, P., & Schyns, P. (2005). The use of
visual information in natural scenes. Visual Cognition, 12, 938 –953.
doi:10.1080/13506280444000599
Mullen, K. T., & Losada, M. A. (1999). The spatial tuning of color and
luminance peripheral vision measured with notch filtered noise masking.
Vision Research, 39, 721–731. doi:10.1016/S0042-6989(98)00171-0
Müller, N. G., Bartelt, O. A., Donner, T. H., Villringer, A., & Brandt, S. A.
(2003). A physiological correlate of the “zoom lens” of visual attention.
The Journal of Neuroscience, 23, 3561–3565.
Nagy, A. L., & Wolf, S. (1993). Red– green color discrimination in
peripheral vision. Vision Research, 33, 235–242. doi:10.1016/00426989(93)90161-O
Nowak, L. G., Munk, M. H. J., Girard, P., & Bullier, J. (1995). Visual
latencies in areas V1 and V2 of the macaque monkey. Visual Neuroscience, 12, 371–384. doi:10.1017/S095252380000804X
Oliva, A., & Schyns, P. G. (1997). Coarse blobs or fine edges? Evidence
that information diagnosticity changes the perception of complex visual
stimuli. Cognitive Psychology, 34, 72–107. doi:10.1006/cogp.1997.0667
Oliva, A., & Schyns, P. G. (2000). Diagnostic colors mediate scene
recognition. Cognitive Psychology, 41, 176 –210. doi:10.1006/cogp
.1999.0728
Oliva, A., & Torralba, A. (2006). Building the gist of a scene: The role of
global image features in recognition. Progress in Brain Research, 155,
23–36.
Osterberg, G. (1935). Topography of the layer of rods and cones in the
human retina. Acta Ophthalmologica (Suppl), 6, 11–96.
Otsuka, S., & Kawaguchi, J. (2007). Natural scene categorization with
minimal attention: Evidence from negative priming. Perception & Psychophysics, 69, 1126 –1139. doi:10.3758/BF03193950
Peli, E., & Geri, G. A. (2001). Discrimination of wide-field images as a test
of a peripheral-vision model. Journal of the Optical Society of America,
A, Optics, Image Science, and Vision, 18, 294 –301. doi:10.1364/JOSAA
.18.000294
Pezdek, K., Whetstone, T., Reynolds, K., Askari, N., & Dougherty, T.
(1989). Memory for real-world scenes: The role of consistency with
schema expectation. Journal of Experimental Psychology: Learning,
Memory, and Cognition, 15, 587–595. doi:10.1037/0278-7393.15.4.587
Pointer, J. S., & Hess, R. F. (1989). The contrast sensitivity gradient across
the human visual field: With emphasis on the low spatial frequency
range. Vision Research, 29, 1133–1151. doi:10.1016/00426989(89)90061-8
Portilla, J., & Simoncelli, E. P. (2000). A parametric texture model based
on joint statistics of complex wavelet coefficients. International Journal
of Computer Vision, 40, 49 –71. doi:10.1023/A:1026553619983
Potter, M. C. (1976). Short-term conceptual memory for pictures. Journal
of Experimental Psychology: Human Learning and Memory, 2, 509 –
522. doi:10.1037/0278-7393.2.5.509
Rayner, K. (1998). Eye movements in reading and information processing:
20 years of research. Psychological Bulletin, 124, 372– 422. doi:
10.1037/0033-2909.124.3.372
Rayner, K., & Bertera, J. H. (1979). Reading without a fovea. Science, 206,
468 – 469. doi:10.1126/science.504987
Rayner, K., Inhoff, A. W., Morrison, R. E., Slowiaczek, M. L., & Bertera,
J. H. (1981). Masking of foveal and parafoveal vision during eye
fixations in reading. Journal of Experimental Psychology: Human Perception and Performance, 7, 167–179. doi:10.1037/0096-1523.7.1.167
Rayner, K., Liversedge, S. P., & White, S. J. (2006). Eye movements when
reading disappearing text: The importance of the word to the right of
fixation. Vision Research, 46, 310 –323. doi:10.1016/j.visres.2005.06
.018
Rayner, K., Liversedge, S. P., White, S. J., & Vergilino-Perez, D. (2003).
Reading disappearing text: Cognitive control of eye movements. Psychological Science, 14, 385–388. doi:10.1111/1467-9280.24483
Rayner, K., Smith, T. J., Malcolm, G. L., & Henderson, J. M. (2009). Eye
movements and visual encoding during scene perception. Psychological
Science, 20, 6 –10. doi:10.1111/j.1467-9280.2008.02243.x
Renninger, L. W., & Malik, J. (2004). When is scene identification just
texture recognition? Vision Research, 44, 2301–2311.
Rouder, J. N., Speckman, P., Sun, D., Morey, R., & Iverson, G. (2009).
Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16, 225–237. doi:10.3758/PBR.16.2.225
Rousselet, G. A., Fabre-Thorpe, M., & Thorpe, S. J. (2002). Parallel
processing in high-level categorization of natural images. Nature Neuroscience, 5, 629.
Rousselet, G. A., Joubert, O. R., & Fabre-Thorpe, M. (2005). How long to
get to the “gist” of real-world natural scenes? Visual Cognition, 12,
852– 877. doi:10.1080/13506280444000553
Rousselet, G. A., Thorpe, S. J., & Fabre-Thorpe, M. (2004). Processing of
one, two or four natural scenes in humans: The limits of parallelism.
Vision Research, 44, 877– 894. doi:10.1016/j.visres.2003.11.014
Rovamo, J., & Iivanainen, A. (1991). Detection of chromatic deviations
from white across the human visual field. Vision Research, 31, 2227–
2234. doi:10.1016/0042-6989(91)90175-5
Schmolesky, M. T., Wang, Y. C., Hanes, D. P., Thompson, K. G., Leutgeb,
S., Schall, J. D., & Leventhal, A. G. (1998). Signal timing across the
macaque visual system. Journal of Neurophysiology, 79, 3272–3278.
Schyns, P., & Oliva, A. (1994). From blobs to boundary edges: Evidence
for time- and spatial-scale-dependent scene recognition. Psychological
Science, 5, 195–200. doi:10.1111/j.1467-9280.1994.tb00500.x
Seiple, W., Clemens, C., Greenstein, V. C., Holopigian, K., & Zhang, X.
(2002). The spatial distribution of selective attention assessed using the
multifocal visual evoked potential. Vision Research, 42, 1513–1521.
doi:10.1016/S0042-6989(02)00079-2
Shimozaki, S. S., Chen, K. Y., Abbey, C. K., & Eckstein, M. P. (2007). The
temporal dynamics of selective attention of the visual periphery as
measured by classification images. Journal of Vision, 7(12), 1–20.
doi:10.1167/7.12.10
Steeves, J. K. E., Humphrey, G. K., Culham, J. C., Menon, R. S., Milner,
A. D., & Goodale, M. A. (2004). Behavioral and neuroimaging evidence
for a contribution of color and texture information to scene classification
in a patient with visual form agnosia. Journal of Cognitive Neuroscience, 16, 955–965. doi:10.1162/0898929041502715
Strasburger, H., Rentschler, I., & Juttner, M. (2011). Peripheral vision and
pattern recognition: A review. Journal of Vision, 11, 13. doi:10.1167/
11.5.13
Thorpe, S. J., Gegenfurtner, K. R., Fabre-Thorpe, M., & Bulthoff, H. H.
(2001). Detection of animals in natural images using far peripheral
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
SPATIOTEMPORAL SCENE GIST DYNAMICS
vision. European Journal of Neuroscience, 14, 869 – 876. doi:10.1046/
j.0953-816x.2001.01717.x
Torralba, A., Oliva, A., Castelhano, M. S., & Henderson, J. M. (2006).
Contextual guidance of eye movements and attention in real-world
scenes: The role of global features in object search. Psychological
Review, 113, 766 –786. doi:10.1037/0033-295X.113.4.766
Tran, T. H. C., Rambaud, C., Despretz, P., & Boucart, M. (2010). Scene
perception in age-related macular degeneration. Investigative Ophthalmology & Visual Science, 51, 6868 – 6874. doi:10.1167/iovs.10-5517
van Diepen, P. M., & d’Ydewalle, G. (2003). Early peripheral and foveal
processing in fixations during scene perception. Visual Cognition, 10,
79 –100. doi:10.1080/713756668
van Diepen, P. M., & Wampers, M. (1998). Scene exploration with
Fourier-filtered peripheral information. Perception, 27, 1141–1151. doi:
10.1068/pp.271141
van Diepen, P. M., Wampers, M., & d’Ydewalle, G. (1998). Functional
division of the visual field: Moving masks and moving windows. In G.
Underwood (Ed.), Eye guidance in reading and scene perception (pp.
337–355). Oxford, UK: Anonima Romana. doi:10.1016/B978008043361-5/50016-X
Walker, S., Stafford, P., & Davis, G. (2008). Ultra-rapid categorization
requires visual attention: Scenes with multiple foreground objects. Journal of Vision, 8, 1–12. doi:10.1167/8.4.21
Westheimer, G. (1982). The spatial grain of the perifoveal visual field.
Vision Research, 22, 157–162. doi:10.1016/0042-6989(82)90177-8
17
Wetzels, R., Matzke, D., Lee, M. D., Rouder, J. N., Iverson, G. J., &
Wagenmakers, E.-J. (2011a). Statistical evidence in experimental psychology: An empirical comparison using 855 t tests. Perspectives on
Psychological Science, 6, 291–298. doi:10.1177/1745691611406923
White, A. L., Rolfs, M., & Carrasco, M. (in press). Adaptive deployment
of spatial and feature-based attention before saccades. Vision Research,
85, 26 –35. doi:10.1016/j.visres.2012.10.017
Wichmann, F. A., Braun, D. I., & Gegenfurtner, K. (2006). Phase noise and
the classification of natural images. Vision Research, 46, 1520 –1529.
doi:10.1016/j.visres.2005.11.008
Wilson, H. R., Levi, D., Maffei, L., Rovamo, J., & DeValois, R. (1990).
The perception of form: Retina to striate cortex. In L. Spillmann & J. S.
Werner (Eds.), Visual perception: The neurophysiological foundations
(pp. 231–272). San Diego, CA: Academic Press.
Yantis, S., & Egeth, H. E. (1999). On the distinction between visual
salience and stimulus-driven attentional capture. Journal of Experimental Psychology: Human Perception and Performance, 25, 661– 676.
doi:10.1037/0096-1523.25.3.661
Yantis, S., & Jonides, J. (1984). Abrupt visual onsets and selective attention: Evidence from visual search. Journal of Experimental Psychology:
Human Perception and Performance, 10, 601– 621. doi:10.1037/00961523.10.5.601
Received October 1, 2012
Revision received September 27, 2013
Accepted October 1, 2013 䡲